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AI Solutions Engineer

Torq
CompanyTorq
CategoryEngineering
LocationUnited States
RemoteOn-site (inferred)
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted27 Jul 2026
Last verified30 Jul 2026
SourceEmployer career page (greenhouse)
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Description
Skeletons, lasers, tattoo buses — the Torq brand grabs attention like nothing else in cybersecurity. And we're growing like crazy, backed by Series D funding, 200% employee growth, and 300% revenue growth. Fueling Torq's momentum is our game-changing AI SOC platform, backed by a team and culture that makes Torq one of Forbes' Best Startup Employers in America, and a Business Insider 'startup to bet your career on'. Life at Torq is all gas, no brakes. We're a team of relentless, collaborative go-getters pushing the boundaries of what's possible for security automation. Every role is an essential driver of Torq's success as the AI-native autonomous SecOps platform of choice for security teams across the Fortune 500.  Skeletons, lasers, tattoo buses - the Torq brand grabs attention like nothing else in cybersecurity. And we're growing like crazy, backed by Series D funding, 200% employee growth, and 300% revenue growth. Fueling Torq's momentum is our game-changing AI SOC platform, backed by a team and culture that makes Torq one of Forbes' Best Startup Employers in America, and a Business Insider 'startup to bet your career on'. Life at Torq is all gas, no brakes. We're a team of relentless, collaborative go-getters pushing the boundaries of what's possible for security automation. Every role is an essential driver of Torq's success as the AI-native autonomous SecOps platform of choice for security teams across the Fortune 500. We're looking for an AI Solutions Engineer to join our Transformation & Business Systems team. This role is built for someone fluent in AI-native building: you know how to vibe code a working tool end-to-end, you're hands-on with at least one automation platform, and you understand how LLMs, agents, and workflows actually fit together. You'll work directly with stakeholders across Finance, HR, Sales, Support, and IT to map manual processes and turn them into automated, AI-powered systems. What You’ll Be Doing Map manual, repetitive business processes end-to-end across the company - Finance, HR, Operations, Marketing, and the revenue funnel - and identify high-impact automation opportunities Own automation delivery from idea to production: translate requirements from non-technical stakeholders and design, build, test, and ship working solutions Build automated workflows using platforms like n8n, Zapier, Make, Tray.io, or Workato Design, build, and deploy AI agents that plug into real business workflows - embedding LLM APIs for use cases like ticket classification, document data extraction, and automated summarization, and applying RAG and memory patterns so they can reason over company knowledge Vibe code complete internal tools end-to-end using AI coding assistants - from rapid prototype (POC) validated with business users to something people actually use in production Connect internal systems (CRM, ERP, ticketing, Google Workspace/Microsoft 365) via APIs and webhooks, including AI-powered workflows for Marketing and Revenue teams — lead enrichment, campaign reporting automation, and pipeline data hygiene Monitor, maintain, and continuously improve existing automations - documenting processes and training internal process owners along the way The Ideal Candidate 3+ years of hands-on experience building automations or AI-powered workflows, including implementing AI/ML tools into operational workflows Solid working knowledge of LLMs - how they work, their capabilities and limitations, and how to get reliable output from them Hands-on, practical experience with at least one AI automation/workflow platform: n8n, Zapier, Make, Tray.io, or Workato Comfortable "vibe coding" - using AI coding tools (e.g. Cursor, Claude Code, Replit) to build and ship a complete project end-to-end Understanding of AI agent workflows - how to design, build, and deploy AI agents - plus working knowledge of RAG (Retrieval-Augmented Generation) and agent memory concepts A
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